High Level Vision Task

High-level vision tasks aim to enable computers to understand and interpret images at a semantic level, going beyond simple feature extraction to tasks like object detection, image segmentation, and scene understanding. Current research focuses on improving the robustness and generalization of these tasks, particularly through the use of transformer-based architectures, multi-modal fusion (e.g., combining infrared and visible data), and unsupervised learning techniques to address data limitations. These advancements are crucial for improving the performance of various applications, including robotics, autonomous driving, and medical image analysis, by bridging the gap between low-level image processing and high-level cognitive understanding.